Search Results

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. This courser will introduce the latest version (2019) of Failure Mode and Effects Analysis (FMEA) Handbook with a focus on DFMEA and PFMEA building. Each column of the FMEA document will also be explained in detail with FMEA examples. The course also includes an introduction to the logic for identifying technical risks and thinking tools for risk mitigation.

This course introduces basic tire mechanics, including tire construction components based on application type, required sidewall stamping in accordance with DoT/ECE regulations, tread patterns, regulatory and research testing on quality, tire inspections and basic tire failure identification. The course will provide you with information that you can use immediately on-the-job and apply to your own vehicle. This course is practical in nature and supplemented with samples and hands-on activities.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

Thermo-mechanical fatigue and natural aging due to environmental conditions are difficult to simulate in an actual test with the advanced fiber-reinforced composites, where their fatigue and aging behavior is little understood. Predictive modeling of these processes is challenging. Thermal cyclic tests take a prohibitively long time, although the strain rate effect can be scaled well for accelerating the mechanical stress cycles. Glass fabric composites have important applications in aircraft and spacecraft structures including microwave transparent structures, impact-resistant parts of wing, fuselage deck and many other load bearing structures. Often additional additively manufactured features and coating on glass fabric composites are employed for thermal and anti-corrosion insulations. In this paper we employ a thermo-mechanical fatigue model based accelerated fatigue test and life prediction under hot to cold cycles.

The Aerospace Industry's drive towards zero defects has seen a significant shift to prevent defects and improve product quality during the design phase, instead of waiting until post-production inspection to discover and troubleshoot problems. Trying to ensure zero defects during the post-production inspection phase is too late in the product life cycle because it can lead to substantial costs. Aerospace Engine Supplier Quality (AESQ) introduced the Advanced Product Quality Planning (APQP) process to realize zero defects. In APQP Phase 2, Product and Design Development, a key output is performing a Design Failure Modes and Effects Analysis (DFMEA). Moog has effectively implemented a DFMEA process that adeptly identifies and mitigates design risks. This paper showcases Moog's successful deployment of DFMEA, exemplifying the industry best practices. This paper also presents simplified and innovative interpretations of DFMEA definitions and approaches.

Abstract: Hydraulic systems in aircrafts largely comprise of metallic components with high strength to weight ratios which comprise of 2024 Aluminum and Titanium Ti-6AL-4V. The selection of material is based on low and high pressure applications respectively. For aircraft fluid conveyance products, hydraulic conduits are fabricated by axisymmetric turning to support flow conditions. The hydraulic conduits further carries groves within for placement of elastomeric sealing components. This article presents a systematic study carried out on common loads experienced by fluid carrying conduits and the failure modes induced. The critical failure locations on fluid carrying conduits of 2024-T351 Aluminum was identified, and the Scanning Electron Microscope (SEM) analysis was carried out to identify the characteristic footprints of failure surfaces and crack initiation. Through this analysis, a load to failure mode correlation is established.

Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviours and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining the outputs of these during the model integration phase, an overall health assessment of the aircraft will be generated.

Contact our Sales Team! The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

The aviation, space, and defense industries rely on the development and manufacture of complex products comprised of multiple systems, subsystems, and components each designed by individual designers (design activities) at various levels within the supply chain. Each design or manufacturing activity controls various aspects of the configuration and specifications related to the product. When a change to design or process is requested or required, the change is typically required to be evaluated against the impacts to the entire system. Proposed changes to design data/information that the design activity identifies to be minor and have no effect on the product requirements or specifications, have the potential to be implemented and approved, where authorized to do so, but requires notification. Changes that affect customer mandated requirements or specifications shall be approved prior to implementation.

This recommended practice is derived from common test sequences used within the industry. This procedure applies to all on-road passenger cars and light trucks up to 4 540 kg of GVWR. This recommended practice does not address other aspects such as performance, NVH, and durability. Test results from this recommended practice should be combined with other measurements and dynamometer tests (or vehicle-level tests), and acceptance criteria to validate a given design or configuration.